import tensorflow as tf
from keras.datasets import mnist
import cv2
import os
import pathlib
from keras.layers import Conv2D, Conv2DTranspose, Dropout, Dense, Reshape, LayerNormalization, LeakyReLU
from keras import layers, models
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import accuracy_score, classification_report
from sklearn.metrics import f1_score, recall_score, precision_score
/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']
caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']
warnings.warn(f"unable to load libtensorflow_io_plugins.so: {e}")
/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']
caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']
warnings.warn(f"file system plugins are not loaded: {e}")
class ReadDataset:
def __init__(self, datasetpath, labels, image_shape):
self.datasetpath = datasetpath
self.labels = labels
self.image_shape = image_shape
def returListImages(self,):
self.images = []
for label in self.labels:
self.images.append(list(pathlib.Path(os.path.join(self.datasetpath,
label)).glob('*.*')))
def readImages(self,):
self.returListImages()
self.finalImages = []
labels = []
for label in range(len(self.labels)):
for img in self.images[label]:
img = cv2.imread(str(img))
img = cv2.resize(img , self.image_shape)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img/255
self.finalImages.append(img)
labels.append(label)
images = np.array(self.finalImages)
labels = np.array(labels)
return images, labels
readDatasetObject = ReadDataset('/kaggle/input/chest-xray-pneumonia/chest_xray/train',
['NORMAL', 'PNEUMONIA'],
(64, 64))
images, labels = readDatasetObject.readImages()
images.shape, labels.shape
((5216, 64, 64, 3), (5216,))
plt.figure(figsize = (12, 12))
indexs = np.random.randint(0, len(labels), size = (64, ))
for i in range(64):
plt.subplot(8, 8, (i + 1))
plt.imshow(images[indexs[i]])
plt.title(labels[indexs[i]])
plt.legend()
<matplotlib.legend.Legend at 0x7c63de2e2d40>
class Acgan:
def __init__(self, eta, batch_size, epochs, weight_decay, latent_space,
image_shape, kernel_size):
self.eta = eta
self.batch_size = batch_size
self.epochs = epochs
self.weight_decay = weight_decay
self.latent_space = latent_space
self.image_shape = image_shape
self.kernel_size = kernel_size
def data(self, images, labels):
ytrain = tf.keras.utils.to_categorical(labels)
self.images = images
self.labels = ytrain
def samples(self, G, noize, labels):
images = G.predict([noize, labels])
ys = np.argmax(labels, axis = 1)
plt.figure(figsize = (12, 4))
for i in range(16):
plt.subplot(2, 8, (i + 1))
plt.imshow(images[i], cmap = 'gray')
plt.title(ys[i])
plt.show()
def generator(self, inputs, labels):
filters = [256, 128, 64, 32]
padding = 'same'
x = inputs
y = labels
x = layers.concatenate([x, y])
x = layers.Dense(1024, )(x)
x = layers.Dense(8*8*filters[0],
kernel_regularizer = tf.keras.regularizers.L2(0.001))(x)
x = layers.Reshape((8, 8, filters[0]))(x)
for filter in filters:
if filter >= 64:
strides = 2
else:
strides = 1
x = LayerNormalization()(x)
x = layers.Activation('relu')(x)
x = Conv2DTranspose(filter, kernel_size = self.kernel_size, padding = padding,
strides = strides)(x)
x = Conv2DTranspose(3, kernel_size = self.kernel_size, padding = padding)(x)
x = layers.Activation('sigmoid')(x)
self.generatorModel = models.Model(inputs = [inputs, labels],
outputs = x,
name = 'generator')
def discriminator(self, inputs):
x = inputs
filters = [32, 64, 128, 256]
padding = 'same'
for filter in filters:
if filter < 256:
strides = 2
else:
strides = 1
x = Conv2D(filter, kernel_size = self.kernel_size, padding = padding,
strides = strides,
kernel_regularizer = tf.keras.regularizers.L2(0.001))(x)
x = LeakyReLU(alpha = 0.2)(x)
x = layers.Flatten()(x)
outputs = Dense(1, )(x)
labelsOutput = Dense(256,
kernel_regularizer = tf.keras.regularizers.L2(0.001))(x)
labelsOutput = Dropout(0.3)(labelsOutput)
labelsOutput = Dense(2,)(labelsOutput)
labelsOutput = layers.Activation('softmax')(labelsOutput)
self.discriminatorModel = models.Model(inputs = inputs,
outputs = [outputs, labelsOutput],
name = 'discriminator')
def build(self,):
generatorInput = layers.Input(shape = (self.latent_space))
discriminatorInput = layers.Input(shape = (self.image_shape))
labelsInput = layers.Input(shape = (2, ))
self.generator(generatorInput, labelsInput)
self.discriminator(discriminatorInput)
G = self.generatorModel
D = self.discriminatorModel
D.compile(loss = ['mse', 'binary_crossentropy'],
optimizer = tf.keras.optimizers.RMSprop(learning_rate = self.eta,
weight_decay = self.weight_decay))
D.summary()
G.summary()
D.trainable = False
GAN = models.Model(inputs = [generatorInput, labelsInput],
outputs = D(G([generatorInput, labelsInput])))
GAN.compile(loss = ['mse', 'binary_crossentropy'],
optimizer = tf.keras.optimizers.RMSprop(learning_rate = self.eta*0.5,
weight_decay = self.weight_decay*0.5))
GAN.summary()
return G, D, GAN
def trainAlgorithm(self, G, D, GAN):
for epoch in range(self.epochs):
indexs = np.random.randint(0, len(self.images), size = (self.batch_size, ))
realImages = self.images[indexs]
realLabels = self.labels[indexs]
realTag = tf.ones(shape = (self.batch_size, ))
noize = tf.random.uniform(shape = (self.batch_size,
self.latent_space), minval = -1,
maxval = 1)
fakeLabels = tf.keras.utils.to_categorical(np.random.choice(range(2), size = (self.batch_size)),
num_classes = 2)
fakeImages = tf.squeeze(G.predict([noize, fakeLabels], verbose = 0))
fakeTag = tf.zeros(shape = (self.batch_size, ))
allImages = np.vstack([realImages, fakeImages])
allLabels = np.vstack([realLabels, fakeLabels])
allTags = np.hstack([realTag, fakeTag])
_, dlossTag, dlossLabels = D.train_on_batch(allImages, [allTags, allLabels])
noize = tf.random.uniform(shape = (self.batch_size,
self.latent_space), minval = -1,
maxval = 1)
_, glossTag, glossLabels = GAN.train_on_batch([noize, fakeLabels], [realTag, fakeLabels])
if epoch % 5000 == 0:
print('Epoch: {}'.format(epoch))
print('discriminator loss: [tag: {}, labels: {}], generator loss: [tag: {}, labels: {}]'.format(dlossTag,
dlossLabels,
glossTag,
glossLabels))
self.samples(G, noize, fakeLabels)
acgan = Acgan(eta = 0.0001, batch_size = 32, epochs = 32000, weight_decay = 6e-9,
latent_space = 100, image_shape = (64, 64, 3), kernel_size = 5)
acgan.data(images, labels)
G, D, GAN = acgan.build()
Model: "discriminator"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 64, 64, 3)] 0 []
conv2d (Conv2D) (None, 32, 32, 32) 2432 ['input_2[0][0]']
leaky_re_lu (LeakyReLU) (None, 32, 32, 32) 0 ['conv2d[0][0]']
conv2d_1 (Conv2D) (None, 16, 16, 64) 51264 ['leaky_re_lu[0][0]']
leaky_re_lu_1 (LeakyReLU) (None, 16, 16, 64) 0 ['conv2d_1[0][0]']
conv2d_2 (Conv2D) (None, 8, 8, 128) 204928 ['leaky_re_lu_1[0][0]']
leaky_re_lu_2 (LeakyReLU) (None, 8, 8, 128) 0 ['conv2d_2[0][0]']
conv2d_3 (Conv2D) (None, 8, 8, 256) 819456 ['leaky_re_lu_2[0][0]']
leaky_re_lu_3 (LeakyReLU) (None, 8, 8, 256) 0 ['conv2d_3[0][0]']
flatten (Flatten) (None, 16384) 0 ['leaky_re_lu_3[0][0]']
dense_3 (Dense) (None, 256) 4194560 ['flatten[0][0]']
dropout (Dropout) (None, 256) 0 ['dense_3[0][0]']
dense_4 (Dense) (None, 2) 514 ['dropout[0][0]']
dense_2 (Dense) (None, 1) 16385 ['flatten[0][0]']
activation_5 (Activation) (None, 2) 0 ['dense_4[0][0]']
==================================================================================================
Total params: 5,289,539
Trainable params: 5,289,539
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "generator"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 100)] 0 []
input_3 (InputLayer) [(None, 2)] 0 []
concatenate (Concatenate) (None, 102) 0 ['input_1[0][0]',
'input_3[0][0]']
dense (Dense) (None, 1024) 105472 ['concatenate[0][0]']
dense_1 (Dense) (None, 16384) 16793600 ['dense[0][0]']
reshape (Reshape) (None, 8, 8, 256) 0 ['dense_1[0][0]']
layer_normalization (LayerNorm (None, 8, 8, 256) 512 ['reshape[0][0]']
alization)
activation (Activation) (None, 8, 8, 256) 0 ['layer_normalization[0][0]']
conv2d_transpose (Conv2DTransp (None, 16, 16, 256) 1638656 ['activation[0][0]']
ose)
layer_normalization_1 (LayerNo (None, 16, 16, 256) 512 ['conv2d_transpose[0][0]']
rmalization)
activation_1 (Activation) (None, 16, 16, 256) 0 ['layer_normalization_1[0][0]']
conv2d_transpose_1 (Conv2DTran (None, 32, 32, 128) 819328 ['activation_1[0][0]']
spose)
layer_normalization_2 (LayerNo (None, 32, 32, 128) 256 ['conv2d_transpose_1[0][0]']
rmalization)
activation_2 (Activation) (None, 32, 32, 128) 0 ['layer_normalization_2[0][0]']
conv2d_transpose_2 (Conv2DTran (None, 64, 64, 64) 204864 ['activation_2[0][0]']
spose)
layer_normalization_3 (LayerNo (None, 64, 64, 64) 128 ['conv2d_transpose_2[0][0]']
rmalization)
activation_3 (Activation) (None, 64, 64, 64) 0 ['layer_normalization_3[0][0]']
conv2d_transpose_3 (Conv2DTran (None, 64, 64, 32) 51232 ['activation_3[0][0]']
spose)
conv2d_transpose_4 (Conv2DTran (None, 64, 64, 3) 2403 ['conv2d_transpose_3[0][0]']
spose)
activation_4 (Activation) (None, 64, 64, 3) 0 ['conv2d_transpose_4[0][0]']
==================================================================================================
Total params: 19,616,963
Trainable params: 19,616,963
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 100)] 0 []
input_3 (InputLayer) [(None, 2)] 0 []
generator (Functional) (None, 64, 64, 3) 19616963 ['input_1[0][0]',
'input_3[0][0]']
discriminator (Functional) [(None, 1), 5289539 ['generator[0][0]']
(None, 2)]
==================================================================================================
Total params: 24,906,502
Trainable params: 19,616,963
Non-trainable params: 5,289,539
__________________________________________________________________________________________________
tf.keras.utils.plot_model(GAN, show_shapes = True)
tf.keras.utils.plot_model(G, show_shapes = True)
tf.keras.utils.plot_model(D, show_shapes = True)
acgan.trainAlgorithm(G, D, GAN)
Epoch: 0 discriminator loss: [tag: 0.5671104788780212, labels: 0.692040741443634], generator loss: [tag: 0.5819922685623169, labels: 0.6864303946495056] 1/1 [==============================] - 0s 21ms/step
Epoch: 5000 discriminator loss: [tag: 0.26543426513671875, labels: 0.03770596161484718], generator loss: [tag: 0.3469369113445282, labels: 0.0006412705988623202] 1/1 [==============================] - 0s 20ms/step
Epoch: 10000 discriminator loss: [tag: 0.24920831620693207, labels: 0.0031062685884535313], generator loss: [tag: 0.2599722146987915, labels: 0.0073606595396995544] 1/1 [==============================] - 0s 24ms/step
Epoch: 15000 discriminator loss: [tag: 0.23986151814460754, labels: 0.020241793245077133], generator loss: [tag: 0.2756638526916504, labels: 0.008874816820025444] 1/1 [==============================] - 0s 20ms/step
Epoch: 20000 discriminator loss: [tag: 0.24258743226528168, labels: 0.0038555630017071962], generator loss: [tag: 0.2774313688278198, labels: 0.0011579369893297553] 1/1 [==============================] - 0s 19ms/step
Epoch: 25000 discriminator loss: [tag: 0.21438449621200562, labels: 0.00024850803310982883], generator loss: [tag: 0.4628139138221741, labels: 0.009382463060319424] 1/1 [==============================] - 0s 25ms/step
Epoch: 30000 discriminator loss: [tag: 0.2039976269006729, labels: 0.000991622800938785], generator loss: [tag: 0.6103872656822205, labels: 8.902423724066466e-05] 1/1 [==============================] - 0s 19ms/step
G.save('/kaggle/working/generator.h5')
G = tf.keras.models.load_model('/kaggle/working/generator.h5')
datasetGenerationSize = 3000
noize = tf.random.uniform(shape = (datasetGenerationSize, 100), minval = -1, maxval = 1)
newlabels = tf.keras.utils.to_categorical(np.random.choice([0, 1], size = (datasetGenerationSize, )), num_classes = 2)
noize.shape, newlabels.shape
(TensorShape([3000, 100]), (3000, 2))
np.unique(np.argmax(newlabels, axis = 1), return_counts = True)
(array([0, 1]), array([1490, 1510]))
imagesGeneration = G.predict([noize, newlabels])
imagesGeneration.shape
94/94 [==============================] - 3s 31ms/step
(3000, 64, 64, 3)
plt.figure(figsize = (12, 12))
t = np.argmax(newlabels, axis = 1)
for i in range(64):
plt.subplot(8, 8, (i + 1))
plt.imshow(imagesGeneration[i])
plt.title(t[i])
plt.legend()
<matplotlib.legend.Legend at 0x7c634035d0c0>
basemodel = tf.keras.applications.VGG16(weights = None, input_shape = (64, 64, 3),
pooling = 'max', include_top = False)
x = layers.Dropout(0.4)(basemodel.output)
x = layers.Dense(128,)(x)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU(alpha = 0.2)(x)
x = layers.Dropout(0.4)(x)
x = layers.Dense(32,)(x)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU(alpha = 0.2)(x)
x = layers.Dropout(0.4)(x)
x = layers.Dense(1, activation = 'sigmoid')(x)
m = tf.keras.models.Model(inputs = basemodel.input, outputs = x)
m.compile(loss = 'binary_crossentropy', optimizer = tf.keras.optimizers.Adam(learning_rate = 0.00001))
m.summary()
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_4 (InputLayer) [(None, 64, 64, 3)] 0
block1_conv1 (Conv2D) (None, 64, 64, 64) 1792
block1_conv2 (Conv2D) (None, 64, 64, 64) 36928
block1_pool (MaxPooling2D) (None, 32, 32, 64) 0
block2_conv1 (Conv2D) (None, 32, 32, 128) 73856
block2_conv2 (Conv2D) (None, 32, 32, 128) 147584
block2_pool (MaxPooling2D) (None, 16, 16, 128) 0
block3_conv1 (Conv2D) (None, 16, 16, 256) 295168
block3_conv2 (Conv2D) (None, 16, 16, 256) 590080
block3_conv3 (Conv2D) (None, 16, 16, 256) 590080
block3_pool (MaxPooling2D) (None, 8, 8, 256) 0
block4_conv1 (Conv2D) (None, 8, 8, 512) 1180160
block4_conv2 (Conv2D) (None, 8, 8, 512) 2359808
block4_conv3 (Conv2D) (None, 8, 8, 512) 2359808
block4_pool (MaxPooling2D) (None, 4, 4, 512) 0
block5_conv1 (Conv2D) (None, 4, 4, 512) 2359808
block5_conv2 (Conv2D) (None, 4, 4, 512) 2359808
block5_conv3 (Conv2D) (None, 4, 4, 512) 2359808
block5_pool (MaxPooling2D) (None, 2, 2, 512) 0
global_max_pooling2d (Globa (None, 512) 0
lMaxPooling2D)
dropout_1 (Dropout) (None, 512) 0
dense_5 (Dense) (None, 128) 65664
batch_normalization (BatchN (None, 128) 512
ormalization)
leaky_re_lu_4 (LeakyReLU) (None, 128) 0
dropout_2 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 32) 4128
batch_normalization_1 (Batc (None, 32) 128
hNormalization)
leaky_re_lu_5 (LeakyReLU) (None, 32) 0
dropout_3 (Dropout) (None, 32) 0
dense_7 (Dense) (None, 1) 33
=================================================================
Total params: 14,785,153
Trainable params: 14,784,833
Non-trainable params: 320
_________________________________________________________________
history = m.fit(imagesGeneration, np.argmax(newlabels, axis = 1),
epochs = 30, batch_size = 64,
validation_split = 0.2,
callbacks = [tf.keras.callbacks.EarlyStopping(patience = 2, monitor = 'val_loss', mode = 'min',
restore_best_weights = True)])
Epoch 1/30 38/38 [==============================] - 17s 157ms/step - loss: 0.6555 - val_loss: 0.6922 Epoch 2/30 38/38 [==============================] - 3s 87ms/step - loss: 0.3790 - val_loss: 0.6904 Epoch 3/30 38/38 [==============================] - 3s 84ms/step - loss: 0.2742 - val_loss: 0.6874 Epoch 4/30 38/38 [==============================] - 3s 84ms/step - loss: 0.2183 - val_loss: 0.6832 Epoch 5/30 38/38 [==============================] - 3s 85ms/step - loss: 0.1750 - val_loss: 0.6757 Epoch 6/30 38/38 [==============================] - 3s 85ms/step - loss: 0.1406 - val_loss: 0.6647 Epoch 7/30 38/38 [==============================] - 3s 86ms/step - loss: 0.1280 - val_loss: 0.6498 Epoch 8/30 38/38 [==============================] - 3s 85ms/step - loss: 0.1272 - val_loss: 0.6199 Epoch 9/30 38/38 [==============================] - 3s 87ms/step - loss: 0.1114 - val_loss: 0.5848 Epoch 10/30 38/38 [==============================] - 3s 85ms/step - loss: 0.0927 - val_loss: 0.5391 Epoch 11/30 38/38 [==============================] - 3s 84ms/step - loss: 0.0875 - val_loss: 0.4780 Epoch 12/30 38/38 [==============================] - 3s 84ms/step - loss: 0.0856 - val_loss: 0.4131 Epoch 13/30 38/38 [==============================] - 3s 84ms/step - loss: 0.0702 - val_loss: 0.3332 Epoch 14/30 38/38 [==============================] - 3s 86ms/step - loss: 0.0725 - val_loss: 0.2718 Epoch 15/30 38/38 [==============================] - 3s 86ms/step - loss: 0.0681 - val_loss: 0.2124 Epoch 16/30 38/38 [==============================] - 3s 84ms/step - loss: 0.0673 - val_loss: 0.1573 Epoch 17/30 38/38 [==============================] - 3s 86ms/step - loss: 0.0649 - val_loss: 0.1204 Epoch 18/30 38/38 [==============================] - 3s 83ms/step - loss: 0.0678 - val_loss: 0.1779 Epoch 19/30 38/38 [==============================] - 3s 86ms/step - loss: 0.0635 - val_loss: 0.1326
plt.figure(figsize = (7, 6))
plt.plot(history.history['loss'], label = 'training loss')
plt.plot(history.history['val_loss'], label = 'validation loss')
plt.title('Results obtained while training a neural network on images generated by the neural network')
plt.legend()
<matplotlib.legend.Legend at 0x7c634056cdf0>
m.evaluate(images, labels)
163/163 [==============================] - 2s 14ms/step - loss: 0.1854
0.18541084229946136
y_pred = tf.squeeze(m.predict(images))
y_pred.shape
163/163 [==============================] - 3s 13ms/step
TensorShape([5216])
y_pred = y_pred >= 0.5
y_pred = np.array(y_pred, dtype = 'int32')
y_pred
array([1, 0, 0, ..., 1, 1, 1], dtype=int32)
accuracy_score(y_pred, labels)*100
95.39877300613497
print(classification_report(y_pred, labels))
precision recall f1-score support
0 0.89 0.93 0.91 1281
1 0.98 0.96 0.97 3935
accuracy 0.95 5216
macro avg 0.93 0.95 0.94 5216
weighted avg 0.96 0.95 0.95 5216
from sklearn.metrics import confusion_matrix
import seaborn as sns
cm = confusion_matrix(y_pred, labels)
cm
array([[1191, 90],
[ 150, 3785]])
import pandas as pd
cmObject = pd.DataFrame(cm , index = ['NORMAL', 'PNEUMONIA'],
columns = ['NORMAL', 'PNEUMONIA'])
cmObject.head()
| NORMAL | PNEUMONIA | |
|---|---|---|
| NORMAL | 1191 | 90 |
| PNEUMONIA | 150 | 3785 |
print('f1_score: {}, recall_score: {}, precision_score: {}'.format(f1_score(y_pred, labels)*100,
recall_score(y_pred, labels)*100,
precision_score(y_pred, labels)*100))
f1_score: 96.9270166453265, recall_score: 96.18805590851333, precision_score: 97.67741935483872
sns.heatmap(cmObject, annot = True, cmap="Blues")
<Axes: >
